





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "convbert"

 def __init__(self							:  List[str]			,					UpperCAmelCase_							:  Any=30_522			,					UpperCAmelCase_							:  Optional[int]=768			,					UpperCAmelCase_							:  Optional[int]=12			,					UpperCAmelCase_							:  Optional[Any]=12			,					UpperCAmelCase_							:  List[str]=3_072			,					UpperCAmelCase_							:  Dict="gelu"			,					UpperCAmelCase_							:  int=0.1			,					UpperCAmelCase_							:  Any=0.1			,					UpperCAmelCase_							:  List[str]=512			,					UpperCAmelCase_							:  List[str]=2			,					UpperCAmelCase_							:  Tuple=0.02			,					UpperCAmelCase_							:  List[str]=1E-1_2			,					UpperCAmelCase_							:  Optional[int]=1			,					UpperCAmelCase_							:  Union[str, Any]=0			,					UpperCAmelCase_							:  Tuple=2			,					UpperCAmelCase_							:  Optional[int]=768			,					UpperCAmelCase_							:  Dict=2			,					UpperCAmelCase_							:  Optional[int]=9			,					UpperCAmelCase_							:  List[str]=1			,					UpperCAmelCase_							:  Union[str, Any]=None			,					**UpperCAmelCase_							:  Optional[int]			,					)			->List[Any]:



   '''simple docstring'''



   super().__init__(
       pad_token_id=UpperCAmelCase_			,					bos_token_id=UpperCAmelCase_			,					eos_token_id=UpperCAmelCase_			,					**UpperCAmelCase_			,					)

   lowerCamelCase__: Any         =vocab_size
   lowerCamelCase__: Optional[Any]         =hidden_size
   lowerCamelCase__: List[Any]         =num_hidden_layers
   lowerCamelCase__: List[str]         =num_attention_heads
   lowerCamelCase__: Dict         =intermediate_size
   lowerCamelCase__: List[Any]         =hidden_act
   lowerCamelCase__: List[str]         =hidden_dropout_prob
   lowerCamelCase__: List[Any]         =attention_probs_dropout_prob
   lowerCamelCase__: Union[str, Any]         =max_position_embeddings
   lowerCamelCase__: str         =type_vocab_size
   lowerCamelCase__: str         =initializer_range
   lowerCamelCase__: Optional[Any]         =layer_norm_eps
   lowerCamelCase__: Optional[int]         =embedding_size
   lowerCamelCase__: List[str]         =head_ratio
   lowerCamelCase__: Optional[Any]         =conv_kernel_size
   lowerCamelCase__: int         =num_groups
   lowerCamelCase__: List[str]         =classifier_dropout



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__: Any         ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__: str         ={0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
       ])






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






